{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3: Problems and Solvers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook covers the basics of setting up and solving problems using Dedalus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll import the public interface and suppress some of the logging messages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dedalus import public as de\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "de.logging_setup.rootlogger.setLevel('ERROR')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1: Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem formulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Systems of differential equations in Dedalus are represented in the form:\n",
    "\n",
    "$$\\mathcal{M} \\cdot \\partial_t \\mathcal{X} + \\mathcal{L} \\cdot \\mathcal{X} = \\mathcal{F}$$\n",
    "\n",
    "where $\\mathcal{X}$ is a state-vector of fields, $\\mathcal{F}$ is a set of generally nonlinear expressions represented by operator trees, and $\\mathcal{M}$ and $\\mathcal{L}$ are matrices of linear differential operators.  This generalized form accomodates prognostic equations, diagnostic constraints, and boundary conditions using the tau method.\n",
    "\n",
    "Dedalus includes a symbolic parser that takes equations and boundary conditions specified in plain text, and manipulates them into the above matrix form.  This form requires the equations to be first-order in time and coupled (Chebyshev) derivatives, and must only contain linear terms on the left-hand-side.  The entire RHS is parsed into an operator tree, and generally contains non-linear terms and linear terms that would couple different Fourier/parity modes, such as non-constant coefficients changing in these directions.\n",
    "\n",
    "To create a problem object, you must provide a domain object and the names of the variables that appear in the equations.  Let's start setting up the KdV-Burgers equation on a finite interval:\n",
    "\n",
    "$$\\partial_t u + u \\partial_x u = a \\partial_{xx} u + b \\partial_{xxx} u$$\n",
    "\n",
    "The KdV-Burgers equation only has one primative field but is third-order in it's derivatives, so we'll have to introduce two extra fields to reduce the equation to first-order.  This problem will use the `IVP` class for initial value problems.  Separate classes are available for linear and nonlinear boundary value problems, and generalized eigenvalue problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create basis and domain\n",
    "x_basis = de.Chebyshev('x', 1024, interval=(-2, 6), dealias=3/2)\n",
    "domain = de.Domain([x_basis], np.float64)\n",
    "\n",
    "# Create problem\n",
    "problem = de.IVP(domain, variables=['u', 'ux', 'uxx'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meta-data and preconditioning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metadata for the problem variables can be specified through the `meta` attribute of the problem object, and indexing by variable name, axis, and property, respectively.  \n",
    "\n",
    "The most common metadata to set here is the `dirichlet` option for Chebyshev bases, which performs a Dirichlet preconditioning / basis-recombination that sparsifies Dirichlet boundary conditions (interpolation at the Chebyshev interval endpoints), at the expense of a slightly increased problem bandwidth.  This can drastically improve performance for problems formulated with only Dirichlet boundary conditions.  Note that because the formulation is first-order in Chebyshev derivatives, this often includes what would be e.g. Neumann boundary conditions in a higher-order formulation.\n",
    "\n",
    "Here we'll apply a Dirichlet preconditioning to all of our variables, for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem.meta[:]['x']['dirichlet'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters and non-constant coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before adding the equations to the problem, we first add any parameters, defined as fields or scalars used in the equations but not part of the state vector of problem variables, to the `problem.parameters` dictionary.\n",
    "\n",
    "For constant/scalar parameters, like we have here, we simply add the desired numerical values to the parameters dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem.parameters['a'] = 2e-4\n",
    "problem.parameters['b'] = 1e-4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For non-constant coefficients, we pass a field object with the desired data.  For linear terms, Dedalus currently only accepts NCCs that couple the Chebyshev direction, i.e. are constant along the Fourier/Parity directions, so that those directions remain linearly uncoupled.  To inform the parser that a NCC will not couple these directions, you must explicitly add some metadata to the NCC fields indiciating that they are constant along the Fourier/Parity directions.  \n",
    "\n",
    "We don't have NCCs or separable dimensions here, but we'll sketch the process here anyways.  Consider a 3D problem on a Fourier (x), SinCos (y), Chebyshev (z) domain.  Here's how we would add a simple non-constant coefficient in z to a problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ncc = domain.new_field(name='c')\n",
    "# ncc['g'] = z**2\n",
    "# ncc.meta['x', 'y']['constant'] = True\n",
    "# problem.paramters['c'] = ncc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Substitutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simplify equation entry, you can define substitution rules, which effectively act as string-replacement rules that will be applied during the parsing process.\n",
    "\n",
    "Substitutions can be used to provide short aliases to quantities computed from the problem variables, and to define shortcut functions similart to python lambda functions, but with normal mathematical-function syntax.  Here's a sketch of how you might define some substitutions that could be useful for a fluid simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Substitution defining the kinetic energy density for a 3D fluid simulation.\n",
    "## Here rho, u, v, and w would be variables in the simulation.\n",
    "\n",
    "# problem.substitutions[‘KE_density’] = \"rho * (u*u + v*v + w*w) / 2\"\n",
    "\n",
    "## Substitution defining the cartesian Laplacian of a field.\n",
    "## Here A and Az are dummy variables that would be replaced by simulation variables in the equations.\n",
    "\n",
    "# problem.substitutions[‘Lap(A, Az)’] = ‘dx(dx(A)) + dy(dy(A)) + dz(Az)’"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equation entry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equations and boundary conditions are then entered in plain text, optionally with conditions specifying which separable modes (indexed by `nx` and `ny` for separable axes named `x` and `y`, etc.) that equation applies to.\n",
    "\n",
    "The parsing namespace basically consists of:\n",
    "* The variables, parameters, and substitutions defined in the problem\n",
    "* The axis names (`'x'` here), representing the individual basis grids\n",
    "* The differential operators for each basis, named as e.g. `'dx'`\n",
    "* The `differentiate`, `integrate`, and `interpolate` factories aliased as `'d'`, `'integ'`, and `'interp'`\n",
    "* `'left'` and `'right'` as aliases to interpolation at the endpoints of the Chebyshev direction, if present\n",
    "* Time and temporal derivatives as `'t'` and `'dt'`, by default (can be modified at IVP instantiation)\n",
    "* Simple mathematical functions (logarithmic and trigonometric), e.g. `'sin'`, `'exp'`, ...\n",
    "\n",
    "Let's see how to enter the equations and boundary conditions for our problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main equation, with linear terms on the LHS and nonlinear terms on the RHS\n",
    "problem.add_equation(\"dt(u) - a*dx(ux) - b*dx(uxx) = -u*ux\")\n",
    "# Auxiliary equations defining the first-order reduction\n",
    "problem.add_equation(\"ux - dx(u) = 0\")\n",
    "problem.add_equation(\"uxx - dx(ux) = 0\")\n",
    "# Boundary conditions\n",
    "problem.add_bc('left(u) = 0')\n",
    "problem.add_bc('left(ux) = 0')\n",
    "problem.add_bc('right(ux) = 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2: Solvers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a solver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each problem type (initial value, linear and nonlinear boundary value, and eigenvalue) has a corresponding solver class that actually performs the solution or iterations for a corresponding problem.  Solvers are simply build using the `problem.build_solver` method.\n",
    "\n",
    "For IVPs, we select a timestepping method when building the solver.  Several multistep and Runge-Kutta IMEX schemes are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = problem.build_solver(de.timesteppers.RK443)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting initial conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fields representing the problem variables can be accessed with a dictionary-like interface through the `solver.state` system.  For IVPs and nonlinear BVPs, initial conditions are set by directly modifying the state variable data before running a simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Field 140402514323664>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reference local grid and state fields\n",
    "x = domain.grid(0)\n",
    "u = solver.state['u']\n",
    "ux = solver.state['ux']\n",
    "uxx = solver.state['uxx']\n",
    "\n",
    "# Setup smooth triangle with support in (-1, 1)\n",
    "n = 20\n",
    "u['g'] = np.log(1 + np.cosh(n)**2/np.cosh(n*x)**2) / (2*n)\n",
    "u.differentiate('x', out=ux)\n",
    "ux.differentiate('x', out=uxx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting stop criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For IVPs, stop criteria for halting time evolution are specified by setting setting the `stop_iteration`, `stop_wall_time` (seconds since solver instantiation), and/or `stop_sim_time` attributes on the solver.  \n",
    "\n",
    "Let's stop after 5000 iterations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop stopping criteria\n",
    "solver.stop_sim_time = np.inf\n",
    "solver.stop_wall_time = np.inf\n",
    "solver.stop_iteration = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving/iterating a problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear BVPs and EVPs are solved using the `solver.solve` method, nonlinear BVPs are iterated using the `solver.newton_iteration` method, and IVPs are iterated using the `solver.step` method with a provided timestep.\n",
    "\n",
    "The logic controlling the main-loop of a Dedalus simulation occurs explicitly in the simulation script.  The `solver.ok` property can be used to halt an evolution loop once any of the specified stopping criteria have been met.  Let's timestep our problem until a halting condition is reached, copying the grid values of `u` every few iterations.  This should take less than a minute on most machines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed iteration 100\n",
      "Completed iteration 200\n",
      "Completed iteration 300\n",
      "Completed iteration 400\n",
      "Completed iteration 500\n",
      "Completed iteration 600\n",
      "Completed iteration 700\n",
      "Completed iteration 800\n",
      "Completed iteration 900\n",
      "Completed iteration 1000\n",
      "Runtime: 7.7894110679626465\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# Setup storage\n",
    "u_list = [np.copy(u['g'])]\n",
    "t_list = [solver.sim_time]\n",
    "\n",
    "# Main loop\n",
    "dt = 1e-2\n",
    "start_time = time.time()\n",
    "while solver.ok:\n",
    "    solver.step(dt)\n",
    "    if solver.iteration % 5 == 0:\n",
    "        u_list.append(np.copy(u['g']))\n",
    "        t_list.append(solver.sim_time)\n",
    "    if solver.iteration % 100 == 0:\n",
    "        print('Completed iteration {}'.format(solver.iteration))\n",
    "end_time = time.time()\n",
    "print('Runtime:', end_time-start_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a space-time plot of the solution on the full dealiased grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A dispersive shock!')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dedalus.extras import plot_tools\n",
    "\n",
    "# Convert storage to arrays\n",
    "u_array = np.array(u_list)\n",
    "t_array = np.array(t_list)\n",
    "\n",
    "# Build space and time meshes\n",
    "x_da = domain.grid(0, scales=domain.dealias)\n",
    "xmesh, ymesh = plot_tools.quad_mesh(x=x_da, y=t_array)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.pcolormesh(xmesh, ymesh, u_array, cmap='RdBu_r')\n",
    "plt.axis(plot_tools.pad_limits(xmesh, ymesh))\n",
    "plt.colorbar()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('t')\n",
    "plt.title('A dispersive shock!')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:dedalus]",
   "language": "python",
   "name": "conda-env-dedalus-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
